1. Field of the Invention
This invention relates to computer systems and, more particularly, to metering resource usage in utility computing environments.
2. Description of the Related Art
Many business organizations and governmental entities rely upon applications that access large amounts of data, often exceeding many terabytes of data, for mission-critical applications. Often, the costs of maintaining the computing and storage resources, as well as the software, required to support such applications may be substantial. Service organizations supporting the application may be required to accurately account for resource usage by different internal and external customers, so that the customers may be billed or charged according to their levels of resource consumption. Some enterprises are gradually moving away from maintaining traditional in-house data centers to leasing or renting resources in external data centers for at least a portion of their computing needs.
In response to the need for pay-per-use accounting of resource usage, a number of “utility computing models” have been developed, in which the use of computing resources such as processors and disks may be “metered” in the way traditional utilities such as electric power and water are metered. One difficulty with such models is the heterogeneity and complexity of mapping resource usage to specific users. Data centers may include hundreds or thousands of devices, any of which may be deployed for use with a variety of complex applications at different times. The resources being used by a particular application may change dynamically and rapidly, and may be spread over a large number of devices. A variety of existing tools and techniques may be available at each device to monitor usage—for example, different tools may be supported on mainframe computer systems than on UNIX-based servers or personal computers. The granularity at which resource usage measurement is possible may also differ from device to device; for example, in some environments, it may be possible to measure the response time of individual disk accesses, while in other environments only summaries or averages of disk access times may be obtained. The amount of resource usage data gathered from different sources via different interfaces, in different units and at different levels of granularity may be so large that it may become difficult to organize and use the data without a unifying framework.
In addition to the heterogeneity-related problems outlined above, the specific data presentation requirements of different customers and internal stakeholders may also have to be taken into account in utility computing environments. Each user may have a different perspective or preference on the manner in which resource usage data is aggregated, and on the units in which resource usage is represented and/or billed. In particular, some users may be interested in a presentation of the costs and resource usage at a business function level, e.g., in terms of specific application services provided, while other users may prefer the resource usage data in terms of individual application programs or at other granularities. Users may also be interested in obtaining different customized views of the same underlying resource usage data, for example, in the ability to drill down into the usage and cost data to identify the specific patterns of usage that are most expensive over a particular time period. A flexible, extensible framework for managing measurement and analysis of resource usage may help to satisfy the requirements of a variety of utility computing customers.